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For Complex Business Processes, RPA Solutions Create Technical Debt

The Market Study Report estimates that the global robotic process automation (RPA) market will see its value approach $12 billion by 2025. That’s a huge jump from just $250 million back in 2016. However, Gartner reports that 50% of RPA software implementations will fail to deliver a sustainable ROI by 2021. Clearly, RPA has its place, but isn’t always the perfect fit. When are RPA solutions the right option, and when are they not the right option? It all comes down to the complexity of the business process being automated.

The Catch-All Appeal of RPA Solutions

Many businesses, especially wholesale manufacturers and distributors, still execute the same manual processes as they did 20 years ago. Take sales order processing. North American companies manually process over $7.5 trillion worth of B2B orders every single year. That’s $7.5-trillion worth of B2B orders being processed by hand, without any help from sophisticated automation technologies. 

Typically, businesses that want to improve their sales order processing turn to RPA solutions because they have seen RPA work with other business processes. They assume that, as RPA has worked elsewhere, there is no reason it won’t work here. RPA is seen as capable of digitally transforming any process that involves repetitive tasks. 

However, when it comes to something like sales order processing, RPA solutions are typically too broad a brush to be successful. 

RPA: Powerful but Limited

RPA offers a toolkit that users can customize to a specific business process. This makes it excel in automating repetitive tasks that only require consistent data inputs and are static in nature. RPA software, however, is not equipped to “learn” and make adjustments. It does not deal well with frequent data, logic and process changes, and is only suitable for consistent instructions. Recognizing changes and updating the script goes beyond the capabilities of an RPA system. 

This is why, with a complex process like sales order processing, RPA often fails. Processes that require more sophisticated cognitive processing need solutions built with artificial intelligence that is designed to learn, respond, and self-correct. These systems can improve and become more efficient in their future processing, while completing the automation and learning more complex logic.

RPA, though, has major limitations. With something like sales order processing, the RPA bots constantly break and fail, and companies are forced to implement heavy oversight to safeguard customer relationships and protect revenue.

The Burden of Technical Debt

When you introduce an RPA solution, but then that RPA solution requires round-the-clock maintenance, technical debt is introduced. With sales order processing, the technical debt that was previously generated by manual sales order processing isn’t ultimately eliminated. Instead, it is simply transferred to the team that now has to see the RPA implementation. Often, this second team is no smaller than the old team. The enterprise finds itself back at square one, with no ROI on their attempted digital transformation.

For example, with sales order processing, companies find themselves needing plenty of resources in place to resolve interruptions ASAP or customer orders and cycle times can be quickly affected. Customer orders are essentially real-time company revenue. One outage could have enormous impacts on a company’s reputation for reliability and the loyalty of their customers.

Complex Processes Need Custom-Built Solutions

Look at it this way. RPA is like growing your own vegetables in your yard. After the effort to lay the beds, plant the seeds and do the regular watering and weeding, you yield less than stellar plants. Isn’t it easier to just go to your local grocer that has fresh, unbruised, perfect produce that has been grown by generations of expert farmers? A custom-built automation solution is that perfect produce.

Sales order automation is one of those complex processes that requires a custom solution that is purpose-built to cope with its subtleties. The technical debt created by an RPA solution is usually burdensome, and runs against the goals of a lean digital transformation.

An effective automated sales order management software solution may still contain some RPA components. This is to deal with the subset of less complicated steps that RPA tools can be trusted with. But automating sales order management cannot be achieved with robotic process automation alone. It needs artificial intelligence that can handle complex business logic. It requires enhanced cognitive capabilities that can course-correct itself and learn from, adapt to, and apply new rules and exceptions in the manufacturing and distribution space.

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